[SPARK-14635][ML] Documentation and Examples for TF-IDF only refer to HashingTF

## What changes were proposed in this pull request?

Currently, the docs for TF-IDF only refer to using HashingTF with IDF. However, CountVectorizer can also be used. We should probably amend the user guide and examples to show this.

## How was this patch tested?

unit tests and doc generation

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #12454 from hhbyyh/tfdoc.

1 files changed, 12 insertions, 3 deletions

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 876d21f495..11d5acbb10 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -22,10 +22,19 @@ This section covers algorithms for working with features, roughly divided into t
 
 [Term Frequency-Inverse Document Frequency (TF-IDF)](http://en.wikipedia.org/wiki/Tf%E2%80%93idf) is a common text pre-processing step.  In Spark ML, TF-IDF is separate into two parts: TF (+hashing) and IDF.
 
-**TF**: `HashingTF` is a `Transformer` which takes sets of terms and converts those sets into fixed-length feature vectors.  In text processing, a "set of terms" might be a bag of words.
-The algorithm combines Term Frequency (TF) counts with the [hashing trick](http://en.wikipedia.org/wiki/Feature_hashing) for dimensionality reduction.
+**TF**: Both `HashingTF` and `CountVectorizer` can be used to generate the term frequency vectors. 
 
-**IDF**: `IDF` is an `Estimator` which fits on a dataset and produces an `IDFModel`.  The `IDFModel` takes feature vectors (generally created from `HashingTF`) and scales each column.  Intuitively, it down-weights columns which appear frequently in a corpus.
+`HashingTF` is a `Transformer` which takes sets of terms and converts those sets into 
+fixed-length feature vectors.  In text processing, a "set of terms" might be a bag of words.
+The algorithm combines Term Frequency (TF) counts with the 
+[hashing trick](http://en.wikipedia.org/wiki/Feature_hashing) for dimensionality reduction.
+
+`CountVectorizer` converts text documents to vectors of term counts. Refer to [CountVectorizer
+](ml-features.html#countvectorizer) for more details.
+
+**IDF**: `IDF` is an `Estimator` which is fit on a dataset and produces an `IDFModel`.  The 
+`IDFModel` takes feature vectors (generally created from `HashingTF` or `CountVectorizer`) and scales each column.  
+Intuitively, it down-weights columns which appear frequently in a corpus.
 
 Please refer to the [MLlib user guide on TF-IDF](mllib-feature-extraction.html#tf-idf) for more details on Term Frequency and Inverse Document Frequency.