Address comments.

Author: viirya

docs/ml-features.md

@@ -783,11 +783,11 @@ Because this existing `OneHotEncoder` is a stateless transformer, it is not usab
 
 ## OneHotEncoderEstimator
 
-[One-hot encoding](http://en.wikipedia.org/wiki/One-hot) maps a column of label indices to a column of binary vectors, and each output binary vector includes at most a single one-value. This encoding allows algorithms which expect continuous features, such as Logistic Regression, to use categorical features. For string type input data, it is common to encode categorical features using [StringIndexer](ml-features.html#stringindexer) first.
+[One-hot encoding](http://en.wikipedia.org/wiki/One-hot) maps a categorical feature, represented as a label index, to a binary vector with at most a single one-value indicating the presence of a specific feature value from among the set of all feature values.
 
-`OneHotEncoderEstimator` can transform multiple columns, returning a one-hot-encoded output vector column for each input column.
+`OneHotEncoderEstimator` can transform multiple columns, returning an one-hot-encoded output vector column for each input column. It is common to merge these vectors into a single feature vector using `VectorAssembler`.
 
-`OneHotEncoderEstimator` supports the `handleInvalid` parameter to choose how to handle invalid input during transforming data. Available options include 'keep' (any invalid inputs are assigned to an extra categorical number) and 'error' (throw an error).
+`OneHotEncoderEstimator` supports the `handleInvalid` parameter to choose how to handle invalid input during transforming data. Available options include 'keep' (any invalid inputs are assigned to an extra categorical index) and 'error' (throw an error).
 
 **Examples**