diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 1a07b76583fca..e5071701b044d 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -1644,6 +1644,7 @@ cdef inline bint is_timedelta(object o):
     return PyDelta_Check(o) or util.is_timedelta64_object(o)
 
 
+@cython.internal
 cdef class Validator:
 
     cdef:
@@ -1662,6 +1663,7 @@ cdef class Validator:
             return False
 
         if self.is_array_typed():
+            # i.e. this ndarray is already of the desired dtype
             return True
         elif self.dtype.type_num == NPY_OBJECT:
             if self.skipna:
@@ -1717,11 +1719,16 @@ cdef class Validator:
         return True
 
     cdef bint finalize_validate_skipna(self):
+        """
+        If we _only_ saw non-dtype-specific NA values, even if they are valid
+        for this dtype, we do not infer this dtype.
+        """
         # TODO(phillipc): Remove the existing validate methods and replace them
         # with the skipna versions upon full deprecation of skipna=False
         return True
 
 
+@cython.internal
 cdef class BoolValidator(Validator):
     cdef inline bint is_value_typed(self, object value) except -1:
         return util.is_bool_object(value)
@@ -1738,6 +1745,7 @@ cpdef bint is_bool_array(ndarray values, bint skipna=False):
     return validator.validate(values)
 
 
+@cython.internal
 cdef class IntegerValidator(Validator):
     cdef inline bint is_value_typed(self, object value) except -1:
         return util.is_integer_object(value)
@@ -1746,6 +1754,7 @@ cdef class IntegerValidator(Validator):
         return issubclass(self.dtype.type, np.integer)
 
 
+# Note: only python-exposed for tests
 cpdef bint is_integer_array(ndarray values):
     cdef:
         IntegerValidator validator = IntegerValidator(len(values),
@@ -1753,6 +1762,7 @@ cpdef bint is_integer_array(ndarray values):
     return validator.validate(values)
 
 
+@cython.internal
 cdef class IntegerNaValidator(Validator):
     cdef inline bint is_value_typed(self, object value) except -1:
         return (util.is_integer_object(value)
@@ -1766,6 +1776,7 @@ cdef bint is_integer_na_array(ndarray values):
     return validator.validate(values)
 
 
+@cython.internal
 cdef class IntegerFloatValidator(Validator):
     cdef inline bint is_value_typed(self, object value) except -1:
         return util.is_integer_object(value) or util.is_float_object(value)
@@ -1781,6 +1792,7 @@ cdef bint is_integer_float_array(ndarray values):
     return validator.validate(values)
 
 
+@cython.internal
 cdef class FloatValidator(Validator):
     cdef inline bint is_value_typed(self, object value) except -1:
         return util.is_float_object(value)
@@ -1789,12 +1801,14 @@ cdef class FloatValidator(Validator):
         return issubclass(self.dtype.type, np.floating)
 
 
+# Note: only python-exposed for tests
 cpdef bint is_float_array(ndarray values):
     cdef:
         FloatValidator validator = FloatValidator(len(values), values.dtype)
     return validator.validate(values)
 
 
+@cython.internal
 cdef class ComplexValidator(Validator):
     cdef inline bint is_value_typed(self, object value) except -1:
         return (
@@ -1812,6 +1826,7 @@ cdef bint is_complex_array(ndarray values):
     return validator.validate(values)
 
 
+@cython.internal
 cdef class DecimalValidator(Validator):
     cdef inline bint is_value_typed(self, object value) except -1:
         return is_decimal(value)
@@ -1823,6 +1838,7 @@ cdef bint is_decimal_array(ndarray values):
     return validator.validate(values)
 
 
+@cython.internal
 cdef class StringValidator(Validator):
     cdef inline bint is_value_typed(self, object value) except -1:
         return isinstance(value, str)
@@ -1843,6 +1859,7 @@ cpdef bint is_string_array(ndarray values, bint skipna=False):
     return validator.validate(values)
 
 
+@cython.internal
 cdef class BytesValidator(Validator):
     cdef inline bint is_value_typed(self, object value) except -1:
         return isinstance(value, bytes)
@@ -1858,6 +1875,7 @@ cdef bint is_bytes_array(ndarray values, bint skipna=False):
     return validator.validate(values)
 
 
+@cython.internal
 cdef class TemporalValidator(Validator):
     cdef:
         Py_ssize_t generic_null_count
@@ -1884,9 +1902,14 @@ cdef class TemporalValidator(Validator):
         return self.is_value_typed(value) or is_typed_null or is_generic_null
 
     cdef inline bint finalize_validate_skipna(self):
+        """
+        If we _only_ saw non-dtype-specific NA values, even if they are valid
+        for this dtype, we do not infer this dtype.
+        """
         return self.generic_null_count != self.n
 
 
+@cython.internal
 cdef class DatetimeValidator(TemporalValidator):
     cdef bint is_value_typed(self, object value) except -1:
         return PyDateTime_Check(value)
@@ -1902,11 +1925,13 @@ cpdef bint is_datetime_array(ndarray values, bint skipna=True):
     return validator.validate(values)
 
 
+@cython.internal
 cdef class Datetime64Validator(DatetimeValidator):
     cdef inline bint is_value_typed(self, object value) except -1:
         return util.is_datetime64_object(value)
 
 
+# Note: only python-exposed for tests
 cpdef bint is_datetime64_array(ndarray values):
     cdef:
         Datetime64Validator validator = Datetime64Validator(len(values),
@@ -1914,7 +1939,7 @@ cpdef bint is_datetime64_array(ndarray values):
     return validator.validate(values)
 
 
-# TODO: only non-here use is in test
+# Note: only python-exposed for tests
 def is_datetime_with_singletz_array(values: ndarray) -> bool:
     """
     Check values have the same tzinfo attribute.
@@ -1945,6 +1970,7 @@ def is_datetime_with_singletz_array(values: ndarray) -> bool:
     return True
 
 
+@cython.internal
 cdef class TimedeltaValidator(TemporalValidator):
     cdef bint is_value_typed(self, object value) except -1:
         return PyDelta_Check(value)
@@ -1953,12 +1979,13 @@ cdef class TimedeltaValidator(TemporalValidator):
         return is_null_timedelta64(value)
 
 
+@cython.internal
 cdef class AnyTimedeltaValidator(TimedeltaValidator):
     cdef inline bint is_value_typed(self, object value) except -1:
         return is_timedelta(value)
 
 
-# TODO: only non-here use is in test
+# Note: only python-exposed for tests
 cpdef bint is_timedelta_or_timedelta64_array(ndarray values):
     """
     Infer with timedeltas and/or nat/none.
@@ -1969,22 +1996,26 @@ cpdef bint is_timedelta_or_timedelta64_array(ndarray values):
     return validator.validate(values)
 
 
+@cython.internal
 cdef class DateValidator(Validator):
     cdef inline bint is_value_typed(self, object value) except -1:
         return PyDate_Check(value)
 
 
+# Note: only python-exposed for tests
 cpdef bint is_date_array(ndarray values, bint skipna=False):
     cdef:
         DateValidator validator = DateValidator(len(values), skipna=skipna)
     return validator.validate(values)
 
 
+@cython.internal
 cdef class TimeValidator(Validator):
     cdef inline bint is_value_typed(self, object value) except -1:
         return PyTime_Check(value)
 
 
+# Note: only python-exposed for tests
 cpdef bint is_time_array(ndarray values, bint skipna=False):
     cdef:
         TimeValidator validator = TimeValidator(len(values), skipna=skipna)
@@ -2022,6 +2053,7 @@ cdef bint is_period_array(ndarray[object] values):
     return True
 
 
+# Note: only python-exposed for tests
 cpdef bint is_interval_array(ndarray values):
     """
     Is this an ndarray of Interval (or np.nan) with a single dtype?