diff --git a/docs/source/cuml-accel/limitations.rst b/docs/source/cuml-accel/limitations.rst
index 4811274f30..0faff1b84d 100644
--- a/docs/source/cuml-accel/limitations.rst
+++ b/docs/source/cuml-accel/limitations.rst
@@ -269,7 +269,6 @@ ElasticNet
 - If ``positive=True``.
 - If ``warm_start=True``.
 - If ``precompute`` is not ``False``.
-- If ``X`` is sparse.
 
 Additionally, the following fitted attributes are currently not computed:
 
@@ -290,7 +289,6 @@ Lasso
 - If ``positive=True``.
 - If ``warm_start=True``.
 - If ``precompute`` is not ``False``.
-- If ``X`` is sparse.
 
 Additionally, the following fitted attributes are currently not computed:
 
diff --git a/python/cuml/cuml/accel/_overrides/sklearn/linear_model.py b/python/cuml/cuml/accel/_overrides/sklearn/linear_model.py
index b80ba0d215..0a6d43f3b8 100644
--- a/python/cuml/cuml/accel/_overrides/sklearn/linear_model.py
+++ b/python/cuml/cuml/accel/_overrides/sklearn/linear_model.py
@@ -10,6 +10,7 @@
 from cuml.accel.estimator_proxy import ProxyBase
 from cuml.common.sparse_utils import is_sparse
 from cuml.internals.array import CumlArray
+from cuml.internals.input_utils import input_to_cuml_array
 from cuml.internals.interop import UnsupportedOnGPU
 from cuml.internals.outputs import using_output_type
 
@@ -34,15 +35,6 @@ class LogisticRegression(ProxyBase):
     _gpu_class = cuml.linear_model.LogisticRegression
 
 
-class ElasticNet(ProxyBase):
-    _gpu_class = cuml.linear_model.ElasticNet
-    _not_implemented_attributes = frozenset(("dual_gap_",))
-
-    def _gpu_fit(self, X, y, sample_weight=None, check_input=True):
-        # Fixes signature mismatch with cuml.ElasticNet. check_input can be ignored.
-        return self._gpu.fit(X, y, sample_weight=sample_weight)
-
-
 class Ridge(ProxyBase):
     _gpu_class = cuml.linear_model.Ridge
 
@@ -74,10 +66,27 @@ def _gpu_fit(self, X, y, sample_weight=None):
         return self
 
 
+class ElasticNet(ProxyBase):
+    _gpu_class = cuml.linear_model.ElasticNet
+    _not_implemented_attributes = frozenset(("dual_gap_",))
+
+    def _gpu_fit(self, X, y, sample_weight=None, check_input=True):
+        # check_input is ignored, only here to fix signature mismatch with sklearn
+
+        y = input_to_cuml_array(y, convert_to_mem_type=False)[0]
+        if len(y.shape) > 1 and y.shape[1] > 1:
+            raise UnsupportedOnGPU("Multi-output targets are not supported")
+        return self._gpu.fit(X, y, sample_weight=sample_weight)
+
+
 class Lasso(ProxyBase):
     _gpu_class = cuml.linear_model.Lasso
     _not_implemented_attributes = frozenset(("dual_gap_",))
 
     def _gpu_fit(self, X, y, sample_weight=None, check_input=True):
-        # Fixes signature mismatch with cuml.Lasso. check_input can be ignored.
+        # check_input is ignored, only here to fix signature mismatch with sklearn
+
+        y = input_to_cuml_array(y, convert_to_mem_type=False)[0]
+        if len(y.shape) > 1 and y.shape[1] > 1:
+            raise UnsupportedOnGPU("Multi-output targets are not supported")
         return self._gpu.fit(X, y, sample_weight=sample_weight)
diff --git a/python/cuml/cuml/linear_model/elastic_net.py b/python/cuml/cuml/linear_model/elastic_net.py
index 9618ba3a5c..baee809668 100644
--- a/python/cuml/cuml/linear_model/elastic_net.py
+++ b/python/cuml/cuml/linear_model/elastic_net.py
@@ -2,8 +2,11 @@
 # SPDX-FileCopyrightText: Copyright (c) 2019-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 #
+import cupyx.scipy.sparse
+
 from cuml.common.array_descriptor import CumlArrayDescriptor
 from cuml.common.doc_utils import generate_docstring
+from cuml.common.sparse_utils import is_sparse
 from cuml.internals.array import CumlArray
 from cuml.internals.base import Base
 from cuml.internals.interop import (
@@ -12,7 +15,11 @@
     to_cpu,
     to_gpu,
 )
-from cuml.internals.mixins import FMajorInputTagMixin, RegressorMixin
+from cuml.internals.mixins import (
+    FMajorInputTagMixin,
+    RegressorMixin,
+    SparseInputTagMixin,
+)
 from cuml.internals.outputs import reflect
 from cuml.linear_model.base import LinearPredictMixin
 from cuml.solvers.cd import fit_coordinate_descent
@@ -20,7 +27,12 @@
 
 
 class ElasticNet(
-    Base, InteropMixin, LinearPredictMixin, RegressorMixin, FMajorInputTagMixin
+    Base,
+    InteropMixin,
+    LinearPredictMixin,
+    RegressorMixin,
+    SparseInputTagMixin,
+    FMajorInputTagMixin,
 ):
     """
     Linear regression with combined L1 and L2 priors as regularizer.
@@ -48,11 +60,12 @@ class ElasticNet(
         The tolerance for the optimization: if the updates are smaller than
         tol, the optimization code checks the dual gap for optimality and
         continues until it is smaller than tol.
-    solver : {'cd', 'qn'}, default='cd'
-        Choose an algorithm:
+    solver : {'auto', 'cd', 'qn'}, default='auto'
+        The solver to use.
 
-          * 'cd' - coordinate descent
-          * 'qn' - quasi-newton
+        - 'auto': uses 'cd' for dense inputs, and 'qn' for sparse inputs
+        - 'cd': uses coordinate descent. Only supports dense inputs.
+        - 'qn': uses quasi-newton methods. Supports sparse and dense inputs.
 
         You may find the alternative 'qn' algorithm is faster when the number
         of features is sufficiently large but the sample size is small.
@@ -76,6 +89,8 @@ class ElasticNet(
     ----------
     coef_ : array, shape (n_features)
         The estimated coefficients for the linear regression model.
+    sparse_coef_ : sparse matrix, shape (n_targets, n_features)
+        Sparse matrix representation of `coef_`.
     intercept_ : float
         The independent term, will be 0 if `fit_intercept` is False.
     n_iter_ : int
@@ -192,7 +207,7 @@ def __init__(
         fit_intercept=True,
         max_iter=1000,
         tol=1e-3,
-        solver="cd",
+        solver="auto",
         selection="cyclic",
         output_type=None,
         verbose=False,
@@ -207,6 +222,12 @@ def __init__(
         self.solver = solver
         self.selection = selection
 
+    @property
+    @reflect
+    def sparse_coef_(self):
+        """Sparse representation of the fitted `coef_`."""
+        return cupyx.scipy.sparse.csr_matrix(self.coef_.to_output("cupy"))
+
     @generate_docstring()
     @reflect(reset=True)
     def fit(
@@ -225,7 +246,11 @@ def fit(
                 f"Expected 0.0 <= l1_ratio <= 1.0, got {self.l1_ratio}"
             )
 
-        if self.solver == "qn":
+        solver = self.solver
+        if solver == "auto":
+            solver = "qn" if is_sparse(X) else "cd"
+
+        if solver == "qn":
             coef, intercept, n_iter, _ = fit_qn(
                 X,
                 y,
@@ -242,7 +267,12 @@ def fit(
             )
             coef = CumlArray(data=coef.to_output("cupy").flatten())
             intercept = intercept.item()
-        elif self.solver == "cd":
+        elif solver == "cd":
+            if is_sparse(X):
+                raise ValueError(
+                    "solver='cd' doesn't support sparse inputs, please use "
+                    "solver='auto' or solver='qn' instead"
+                )
             coef, intercept, n_iter = fit_coordinate_descent(
                 X,
                 y,
@@ -256,7 +286,7 @@ def fit(
                 tol=self.tol,
             )
         else:
-            raise ValueError(f"solver {self.solver} is not supported")
+            raise ValueError(f"solver={solver!r} is not supported")
 
         self.coef_ = coef
         self.intercept_ = intercept
diff --git a/python/cuml/cuml/linear_model/lasso.py b/python/cuml/cuml/linear_model/lasso.py
index 9c80630c98..bbadb2f684 100644
--- a/python/cuml/cuml/linear_model/lasso.py
+++ b/python/cuml/cuml/linear_model/lasso.py
@@ -30,14 +30,15 @@ class Lasso(ElasticNet):
         The tolerance for the optimization: if the updates are smaller than
         tol, the optimization code checks the dual gap for optimality and
         continues until it is smaller than tol.
-    solver : {'cd', 'qn'} (default='cd')
-        Choose an algorithm:
+    solver : {'auto', 'cd', 'qn'}, default='auto'
+        The solver to use.
 
-          * 'cd' - coordinate descent
-          * 'qn' - quasi-newton
+        - 'auto': uses 'cd' for dense inputs, and 'qn' for sparse inputs
+        - 'cd': uses coordinate descent. Only supports dense inputs.
+        - 'qn': uses quasi-newton methods. Supports sparse and dense inputs.
 
         You may find the alternative 'qn' algorithm is faster when the number
-        of features is sufficiently large, but the sample size is small.
+        of features is sufficiently large but the sample size is small.
     selection : {'cyclic', 'random'} (default='cyclic')
         If set to 'random', a random coefficient is updated every iteration
         rather than looping over features sequentially by default.
@@ -57,6 +58,8 @@ class Lasso(ElasticNet):
     ----------
     coef_ : array, shape (n_features)
         The estimated coefficients for the linear regression model.
+    sparse_coef_ : sparse matrix, shape (n_targets, n_features)
+        Sparse matrix representation of `coef_`.
     intercept_ : array
         The independent term. If `fit_intercept` is False, will be 0.
     n_iter_ : int
@@ -119,7 +122,7 @@ def __init__(
         fit_intercept=True,
         max_iter=1000,
         tol=1e-3,
-        solver="cd",
+        solver="auto",
         selection="cyclic",
         output_type=None,
         verbose=False,
diff --git a/python/cuml/cuml_accel_tests/upstream/scikit-learn/xfail-list.yaml b/python/cuml/cuml_accel_tests/upstream/scikit-learn/xfail-list.yaml
index 611d5afd14..8d1edcf3a3 100644
--- a/python/cuml/cuml_accel_tests/upstream/scikit-learn/xfail-list.yaml
+++ b/python/cuml/cuml_accel_tests/upstream/scikit-learn/xfail-list.yaml
@@ -33,10 +33,16 @@
   tests:
   - "sklearn.linear_model.tests.test_coordinate_descent::test_enet_coordinate_descent[Lasso-1-kwargs1]"
   - "sklearn.linear_model.tests.test_coordinate_descent::test_warm_start_convergence"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-False-24-6-False-Lasso]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-False-24-6-True-Lasso]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-False-6-24-False-ElasticNet]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-False-6-24-True-ElasticNet]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-False-24-6-False-Lasso]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-False-24-6-True-Lasso]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-False-6-24-False-ElasticNet]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-False-6-24-True-ElasticNet]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_enet_coordinate_descent[csc_array]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_enet_coordinate_descent[csc_matrix]"
   - "sklearn.manifold.tests.test_t_sne::test_pca_initialization_not_compatible_with_sparse_input[csr_array]"
   - "sklearn.manifold.tests.test_t_sne::test_pca_initialization_not_compatible_with_sparse_input[csr_matrix]"
 - reason: Test should fail with cuml.accel
@@ -235,7 +241,6 @@
   - "sklearn.linear_model.tests.test_base::test_linear_regression"
   - "sklearn.linear_model.tests.test_base::test_linear_regression_pd_sparse_dataframe_warning"
   - "sklearn.linear_model.tests.test_common::test_balance_property[42-True-LogisticRegression]"
-  - "sklearn.linear_model.tests.test_coordinate_descent::test_check_input_false"
   - "sklearn.linear_model.tests.test_coordinate_descent::test_elasticnet_precompute_gram_weighted_samples"
   - "sklearn.linear_model.tests.test_coordinate_descent::test_enet_copy_X_False_check_input_False"
   - "sklearn.linear_model.tests.test_coordinate_descent::test_enet_float_precision"
@@ -243,6 +248,10 @@
   - "sklearn.linear_model.tests.test_coordinate_descent::test_enet_nonfinite_params"
   - "sklearn.linear_model.tests.test_coordinate_descent::test_enet_sample_weight_consistency[42-None-False-0.01-False]"
   - "sklearn.linear_model.tests.test_coordinate_descent::test_enet_sample_weight_consistency[42-None-False-0.01-True]"
+  - "sklearn.linear_model.tests.test_coordinate_descent::test_enet_sample_weight_consistency[42-csr_array-False-0.01-False]"
+  - "sklearn.linear_model.tests.test_coordinate_descent::test_enet_sample_weight_consistency[42-csr_array-False-0.01-True]"
+  - "sklearn.linear_model.tests.test_coordinate_descent::test_enet_sample_weight_consistency[42-csr_matrix-False-0.01-False]"
+  - "sklearn.linear_model.tests.test_coordinate_descent::test_enet_sample_weight_consistency[42-csr_matrix-False-0.01-True]"
   - "sklearn.linear_model.tests.test_coordinate_descent::test_enet_toy"
   - "sklearn.linear_model.tests.test_coordinate_descent::test_lassoCV_does_not_set_precompute[False-False]"
   - "sklearn.linear_model.tests.test_coordinate_descent::test_lassoCV_does_not_set_precompute[auto-False]"
@@ -251,6 +260,8 @@
   - "sklearn.linear_model.tests.test_coordinate_descent::test_lasso_readonly_data"
   - "sklearn.linear_model.tests.test_coordinate_descent::test_lasso_toy"
   - "sklearn.linear_model.tests.test_coordinate_descent::test_lasso_zero"
+  - "sklearn.linear_model.tests.test_coordinate_descent::test_sparse_input_convergence_warning[csr_array]"
+  - "sklearn.linear_model.tests.test_coordinate_descent::test_sparse_input_convergence_warning[csr_matrix]"
   - "sklearn.linear_model.tests.test_coordinate_descent::test_warm_start_convergence_with_regularizer_decrement"
   - "sklearn.linear_model.tests.test_ransac::test_perfect_horizontal_line"
   - "sklearn.linear_model.tests.test_ransac::test_ransac_exceed_max_skips"
@@ -273,16 +284,36 @@
   - "sklearn.linear_model.tests.test_ridge::test_ridge_sample_weight_consistency[42-saga-wide-csr_matrix-False]"
   - "sklearn.linear_model.tests.test_ridge::test_ridgecv_sample_weight"
   - "sklearn.linear_model.tests.test_sag::test_step_size_alpha_error"
-  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_same_multiple_output_sparse_dense[coo_array]"
-  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_same_multiple_output_sparse_dense[coo_matrix]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_multitarget[csc_array]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_multitarget[csc_matrix]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_toy_explicit_sparse_input[lil_array]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_toy_explicit_sparse_input[lil_matrix]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_toy_list_input[csc_array-False]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_toy_list_input[csc_array-True]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_toy_list_input[csc_matrix-False]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_toy_list_input[csc_matrix-True]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_lasso_zero[csc_array]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_lasso_zero[csc_matrix]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-False-6-24-False-ElasticNet]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-False-6-24-False-Lasso]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-False-6-24-True-ElasticNet]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-False-6-24-True-Lasso]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-True-24-6-False-ElasticNet]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-True-24-6-False-Lasso]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-True-24-6-True-ElasticNet]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-True-24-6-True-Lasso]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-True-6-24-False-ElasticNet]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-True-6-24-False-Lasso]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-True-6-24-True-ElasticNet]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-True-6-24-True-Lasso]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-False-6-24-False-ElasticNet]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-False-6-24-False-Lasso]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-False-6-24-True-ElasticNet]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-False-6-24-True-Lasso]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-True-24-6-False-ElasticNet]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-True-24-6-False-Lasso]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-True-24-6-True-ElasticNet]"
+  - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-True-24-6-True-Lasso]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-True-6-24-False-ElasticNet]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-True-6-24-False-Lasso]"
   - "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-True-6-24-True-ElasticNet]"
@@ -728,6 +759,8 @@
   - "sklearn.neighbors.tests.test_neighbors::test_neighbor_classifiers_loocv[ball_tree-nn_model0]"
   - "sklearn.neighbors.tests.test_neighbors::test_neighbor_classifiers_loocv[brute-nn_model0]"
   - "sklearn.neighbors.tests.test_neighbors::test_neighbor_classifiers_loocv[kd_tree-nn_model0]"
+  - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_sample_weight_equivalence_on_sparse_data]"
+  - "sklearn.tests.test_common::test_estimators[Lasso()-check_sample_weight_equivalence_on_sparse_data]"
   - "sklearn.tests.test_common::test_estimators[LogisticRegression()-check_sample_weight_equivalence_on_dense_data]"
   - "sklearn.tests.test_common::test_estimators[LogisticRegression()-check_sample_weight_equivalence_on_sparse_data]"
   - "sklearn.tests.test_common::test_estimators[Ridge()-check_non_transformer_estimators_n_iter]"
@@ -1067,7 +1100,6 @@
   - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_estimators_nan_inf]"
   - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_regressor_data_not_an_array]"
-  - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_regressor_multioutput]"
   - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_requires_y_none]"
   - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_sample_weights_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_supervised_y_no_nan]"
@@ -1093,7 +1125,6 @@
   - "sklearn.tests.test_common::test_estimators[Lasso()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[Lasso()-check_estimators_nan_inf]"
   - "sklearn.tests.test_common::test_estimators[Lasso()-check_regressor_data_not_an_array]"
-  - "sklearn.tests.test_common::test_estimators[Lasso()-check_regressor_multioutput]"
   - "sklearn.tests.test_common::test_estimators[Lasso()-check_requires_y_none]"
   - "sklearn.tests.test_common::test_estimators[Lasso()-check_sample_weights_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[Lasso()-check_supervised_y_no_nan]"
@@ -1266,6 +1297,14 @@
   tests:
   - "sklearn.tests.test_calibration::test_calibrated_classifier_cv_double_sample_weights_equivalence[False-temperature]"
   - "sklearn.tests.test_calibration::test_calibrated_classifier_cv_double_sample_weights_equivalence[True-temperature]"
+- reason: ElasticNet sample_weight handling is a bit off
+  condition: scikit-learn>=1.6,<1.8
+  tests:
+  - "sklearn.linear_model.tests.test_coordinate_descent::test_enet_alpha_max_sample_weight[sample_weight0-False-True]"
+- reason: ElasticNet sample_weight handling is a bit off
+  condition: scikit-learn>=1.8
+  tests:
+  - "sklearn.linear_model.tests.test_coordinate_descent::test_enet_alpha_max[sample_weight0-False-True]"
 - reason: Elasticnet scores attribute layout differs with cuml.accel in sklearn 1.8
   condition: scikit-learn>=1.8
   tests:
diff --git a/python/cuml/tests/test_coordinate_descent.py b/python/cuml/tests/test_elastic_net.py
similarity index 75%
rename from python/cuml/tests/test_coordinate_descent.py
rename to python/cuml/tests/test_elastic_net.py
index 6588d5f419..9f784f8165 100644
--- a/python/cuml/tests/test_coordinate_descent.py
+++ b/python/cuml/tests/test_elastic_net.py
@@ -4,15 +4,61 @@
 
 import numpy as np
 import pytest
+import scipy.sparse
+import sklearn.linear_model
+from hypothesis import example, given
+from hypothesis import strategies as st
 from sklearn.datasets import make_regression
 from sklearn.linear_model import ElasticNet, Lasso
 from sklearn.model_selection import train_test_split
 
 import cuml
 from cuml.metrics import r2_score
+from cuml.testing.datasets import make_regression_dataset
+from cuml.testing.strategies import dataset_dtypes
 from cuml.testing.utils import quality_param, stress_param, unit_param
 
 
+@given(
+    datatype=dataset_dtypes(),
+    alpha=st.sampled_from([0.1, 1.0, 10.0]),
+    l1_ratio=st.sampled_from([0.1, 0.5, 0.9]),
+    nrows=st.integers(min_value=1000, max_value=5000),
+    column_info=st.sampled_from([[20, 10], [100, 50]]),
+)
+@example(
+    datatype=np.float32,
+    alpha=0.1,
+    l1_ratio=0.1,
+    nrows=1000,
+    column_info=[20, 10],
+)
+@example(
+    datatype=np.float64,
+    alpha=10.0,
+    l1_ratio=0.9,
+    nrows=5000,
+    column_info=[100, 50],
+)
+def test_elastic_net_solvers_eq(datatype, alpha, l1_ratio, nrows, column_info):
+    ncols, n_info = column_info
+    X_train, X_test, y_train, y_test = make_regression_dataset(
+        datatype, nrows, ncols, n_info
+    )
+
+    kwargs = {"alpha": alpha, "l1_ratio": l1_ratio}
+    cd = cuml.ElasticNet(solver="cd", **kwargs)
+    cd.fit(X_train, y_train)
+    cd_res = cd.predict(X_test)
+
+    qn = cuml.ElasticNet(solver="qn", **kwargs)
+    qn.fit(X_train, y_train)
+    # the results of the two models should be close (even if both are bad)
+    assert qn.score(X_test, cd_res) > 0.90
+    # coefficients of the two models should be close
+    assert np.corrcoef(cd.coef_, qn.coef_)[0, 1] > 0.98
+
+
 @pytest.mark.parametrize("datatype", [np.float32, np.float64])
 @pytest.mark.parametrize("alpha", [0.1, 0.001])
 @pytest.mark.parametrize("algorithm", ["cyclic", "random"])
@@ -301,3 +347,54 @@ def test_max_iter_n_iter(cls, solver):
 
     model = cls(max_iter=2).fit(X, y)
     assert model.n_iter_ == 2
+
+
+def make_sparse_regression(
+    n_samples=1000, n_features=100, n_informative=10, seed=42, dtype="float64"
+):
+    rng = np.random.default_rng(seed)
+
+    w = rng.normal(size=(n_features, 1))
+    w[n_informative:] = 0.0
+
+    X = rng.normal(size=(n_samples, n_features))
+    rnd = rng.uniform(size=(n_samples, n_features))
+    X[rnd > 0.5] = 0.0
+
+    y = np.dot(X, w).ravel().astype(dtype)
+    X = scipy.sparse.csr_matrix(X).astype(dtype)
+    return X, y
+
+
+@pytest.mark.parametrize("dtype", ["float32", "float64"])
+@pytest.mark.parametrize("alpha", [0.2, 0.7])
+@pytest.mark.parametrize("model_name", ["ElasticNet", "Lasso"])
+def test_sparse(dtype, alpha, model_name):
+    X, y = make_sparse_regression(dtype=dtype)
+
+    cu_cls = getattr(cuml.linear_model, model_name)
+    sk_cls = getattr(sklearn.linear_model, model_name)
+
+    cu_model = cu_cls(alpha=alpha, tol=1e-10).fit(X, y)
+    sk_model = sk_cls(alpha=alpha).fit(X, y)
+
+    np.testing.assert_allclose(cu_model.coef_, sk_model.coef_, atol=1e-3)
+    np.testing.assert_allclose(
+        cu_model.intercept_, sk_model.intercept_, atol=1e-3
+    )
+
+    assert isinstance(cu_model.sparse_coef_, scipy.sparse.csr_matrix)
+
+    cu_score = cu_model.score(X, y)
+    sk_score = sk_model.score(X, y)
+    assert cu_score >= sk_score - 0.1
+
+
+def test_solver_errors():
+    X, y = make_sparse_regression()
+
+    with pytest.raises(ValueError, match="solver='bad' is not supported"):
+        cuml.ElasticNet(solver="bad").fit(X, y)
+
+    with pytest.raises(ValueError, match="solver='cd' doesn't support sparse"):
+        cuml.ElasticNet(solver="cd").fit(X, y)
diff --git a/python/cuml/tests/test_exceptions.py b/python/cuml/tests/test_exceptions.py
index f114c59cba..114cd8466b 100644
--- a/python/cuml/tests/test_exceptions.py
+++ b/python/cuml/tests/test_exceptions.py
@@ -11,15 +11,12 @@
 
 from cuml.cluster import DBSCAN, HDBSCAN, KMeans
 from cuml.decomposition import TruncatedSVD
-from cuml.linear_model import ElasticNet, Lasso
 
 # Estimators that raise TypeError when given sparse input (they don't support sparse)
 estimators = {
     "KMeans": lambda: KMeans(n_clusters=2, random_state=0),
     "DBSCAN": lambda: DBSCAN(eps=1.0),
     "TruncatedSVD": lambda: TruncatedSVD(n_components=1, random_state=0),
-    "ElasticNet": lambda: ElasticNet(),
-    "Lasso": lambda: Lasso(),
     "HDBSCAN": lambda: HDBSCAN(),
 }
 
diff --git a/python/cuml/tests/test_linear_model.py b/python/cuml/tests/test_linear_model.py
index da0dd0e0f6..01727203cc 100644
--- a/python/cuml/tests/test_linear_model.py
+++ b/python/cuml/tests/test_linear_model.py
@@ -14,13 +14,11 @@
 from hypothesis import target
 from scipy.sparse import csr_matrix
 from sklearn.datasets import load_breast_cancer, load_digits
-from sklearn.linear_model import ElasticNet as skElasticNet
 from sklearn.linear_model import LinearRegression as skLinearRegression
 from sklearn.linear_model import LogisticRegression as skLog
 from sklearn.model_selection import train_test_split
 
 import cuml
-from cuml import ElasticNet as cuElasticNet
 from cuml import LinearRegression as cuLinearRegression
 from cuml import LogisticRegression as cuLog
 from cuml.testing.datasets import (
@@ -859,46 +857,6 @@ def test_logistic_regression_max_iter_n_iter(penalty):
     assert model.n_iter_.max() == 10
 
 
-@given(
-    datatype=dataset_dtypes(),
-    alpha=st.sampled_from([0.1, 1.0, 10.0]),
-    l1_ratio=st.sampled_from([0.1, 0.5, 0.9]),
-    nrows=st.integers(min_value=1000, max_value=5000),
-    column_info=st.sampled_from([[20, 10], [100, 50]]),
-)
-@example(
-    datatype=np.float32,
-    alpha=0.1,
-    l1_ratio=0.1,
-    nrows=1000,
-    column_info=[20, 10],
-)
-@example(
-    datatype=np.float64,
-    alpha=10.0,
-    l1_ratio=0.9,
-    nrows=5000,
-    column_info=[100, 50],
-)
-def test_elasticnet_solvers_eq(datatype, alpha, l1_ratio, nrows, column_info):
-    ncols, n_info = column_info
-    X_train, X_test, y_train, y_test = make_regression_dataset(
-        datatype, nrows, ncols, n_info
-    )
-
-    kwargs = {"alpha": alpha, "l1_ratio": l1_ratio}
-    cd = cuElasticNet(solver="cd", **kwargs)
-    cd.fit(X_train, y_train)
-    cd_res = cd.predict(X_test)
-
-    qn = cuElasticNet(solver="qn", **kwargs)
-    qn.fit(X_train, y_train)
-    # the results of the two models should be close (even if both are bad)
-    assert qn.score(X_test, cd_res) > 0.90
-    # coefficients of the two models should be close
-    assert np.corrcoef(cd.coef_, qn.coef_)[0, 1] > 0.98
-
-
 @pytest.mark.filterwarnings("ignore:Changing solver.*:UserWarning")
 @given(
     algo=st.sampled_from(["eig", "qr", "svd", "svd-qr", "lsmr"]),
@@ -1024,58 +982,3 @@ def test_linear_regression_sparse(dtype, fit_intercept, weighted, n_targets):
 
     # Check predictions are close
     np.testing.assert_allclose(cu_pred, sk_pred, atol=1e-2)
-
-
-@given(
-    ntargets=st.integers(min_value=1, max_value=2),
-    datatype=dataset_dtypes(),
-    solver=st.sampled_from(["cd", "qn"]),
-    nrows=st.integers(min_value=1000, max_value=5000),
-    column_info=st.sampled_from([[20, 10], [100, 50]]),
-)
-@example(
-    ntargets=1,
-    datatype=np.float32,
-    solver="cd",
-    nrows=1000,
-    column_info=[20, 10],
-)
-@example(
-    ntargets=2,
-    datatype=np.float64,
-    solver="qn",
-    nrows=5000,
-    column_info=[100, 50],
-)
-def test_elasticnet_model(datatype, solver, nrows, column_info, ntargets):
-    ncols, n_info = column_info
-    X_train, X_test, y_train, y_test = make_regression_dataset(
-        datatype, nrows, ncols, n_info, n_targets=ntargets
-    )
-
-    # Initialization of cuML's elastic net model
-    cuelastic = cuElasticNet(alpha=0.1, l1_ratio=0.5, solver=solver)
-
-    if ntargets > 1:
-        with pytest.raises(ValueError, match="Expected 1 columns"):
-            cuelastic.fit(X_train, y_train)
-        return
-
-    # fit and predict cuml elastic net model
-    cuelastic.fit(X_train, y_train)
-    cuelastic_predict = cuelastic.predict(X_test)
-
-    if nrows < 500000:
-        # sklearn elastic net model initialization, fit and predict
-        skelastic = skElasticNet(alpha=0.1, l1_ratio=0.5)
-        skelastic.fit(X_train, y_train)
-
-        skelastic_predict = skelastic.predict(X_test)
-
-        assert array_equal(
-            skelastic_predict,
-            cuelastic_predict,
-            3e-0,
-            total_tol=1e-0,
-            with_sign=True,
-        )
diff --git a/python/cuml/tests/test_sklearn_compatibility.py b/python/cuml/tests/test_sklearn_compatibility.py
index 22922dad41..287abd2360 100644
--- a/python/cuml/tests/test_sklearn_compatibility.py
+++ b/python/cuml/tests/test_sklearn_compatibility.py
@@ -350,6 +350,7 @@
         "check_supervised_y_2d": "Lasso does not handle 2D y",
         "check_supervised_y_no_nan": "Lasso does not check for NaN in y",
         "check_requires_y_none": "Lasso does not handle y=None",
+        "check_sample_weight_equivalence_on_sparse_data": "Lasso QN solver has issues with sample weights",
     },
     ElasticNet: {
         "check_estimator_tags_renamed": "No support for modern tags infrastructure",
@@ -365,6 +366,7 @@
         "check_supervised_y_2d": "ElasticNet does not handle 2D y",
         "check_supervised_y_no_nan": "ElasticNet does not check for NaN in y",
         "check_requires_y_none": "ElasticNet does not handle y=None",
+        "check_sample_weight_equivalence_on_sparse_data": "ElasticNet QN solver has issues with sample weights",
     },
     KernelDensity: {
         "check_estimator_tags_renamed": "No support for modern tags infrastructure",