[SPARK-18795][ML][SPARKR][DOC] Added KSTest section to SparkR vignettes

## What changes were proposed in this pull request?

Added short section for KSTest.
Also added logreg model to list of ML models in vignette.  (This will be reorganized under SPARK-18849)

![screen shot 2016-12-14 at 1 37 31 pm](https://cloud.githubusercontent.com/assets/5084283/21202140/7f24e240-c202-11e6-9362-458208bb9159.png

)

## How was this patch tested?

Manually tested example locally.
Built vignettes locally.

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #16283 from jkbradley/ksTest-vignette.

(cherry picked from commit 78627425)
Signed-off-by: Joseph K. Bradley <joseph@databricks.com>

R/pkg/vignettes/sparkr-vignettes.Rmd

@@ -469,6 +469,10 @@ SparkR supports the following machine learning models and algorithms.
 
 * Isotonic Regression Model
 
+* Logistic Regression Model
+
+* Kolmogorov-Smirnov Test
+
 More will be added in the future.
 
 ### R Formula
@@ -800,7 +804,7 @@ newDF <- createDataFrame(data.frame(x = c(1.5, 3.2)))
 head(predict(isoregModel, newDF))
 ```
 
-### Logistic Regression Model
+#### Logistic Regression Model
 
 (Added in 2.1.0)
 
@@ -834,6 +838,29 @@ model <- spark.logit(df, Species ~ ., regParam = 0.5)
 summary(model)
 ```
 
+#### Kolmogorov-Smirnov Test
+
+`spark.kstest` runs a two-sided, one-sample [Kolmogorov-Smirnov (KS) test](https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test).
+Given a `SparkDataFrame`, the test compares continuous data in a given column `testCol` with the theoretical distribution
+specified by parameter `nullHypothesis`.
+Users can call `summary` to get a summary of the test results.
+
+In the following example, we test whether the `longley` dataset's `Armed_Forces` column
+follows a normal distribution.  We set the parameters of the normal distribution using
+the mean and standard deviation of the sample.
+
+```{r, warning=FALSE}
+df <- createDataFrame(longley)
+afStats <- head(select(df, mean(df$Armed_Forces), sd(df$Armed_Forces)))
+afMean <- afStats[1]
+afStd <- afStats[2]
+
+test <- spark.kstest(df, "Armed_Forces", "norm", c(afMean, afStd))
+testSummary <- summary(test)
+testSummary
+```
+
+
 ### Model Persistence
 The following example shows how to save/load an ML model by SparkR.
 ```{r, warning=FALSE}