[SPARK-19762][ML][FOLLOWUP]Add necessary comments to L2Regularization.

mllib/src/main/scala/org/apache/spark/ml/optim/loss/DifferentiableRegularization.scala

@@ -39,9 +39,13 @@ private[ml] trait DifferentiableRegularization[T] extends DiffFunction[T] {
  *
  * @param regParam The magnitude of the regularization.
  * @param shouldApply A function (Int => Boolean) indicating whether a given index should have
- *                    regularization applied to it.
+ *                    regularization applied to it. Usually we don't apply regularization to
+ *                    the intercept.
  * @param applyFeaturesStd Option for a function which maps coefficient index (column major) to the
- *                         feature standard deviation. If `None`, no standardization is applied.
+ *                         feature standard deviation. Since we always standardize the data during
+ *                         training, if `standardization` is false, we have to reverse
+ *                         standardization by penalizing each component differently by this param.
+ *                         If `standardization` is true, this should be `None`.
  */
 private[ml] class L2Regularization(
     override val regParam: Double,
@@ -57,6 +61,11 @@ private[ml] class L2Regularization(
           val coef = coefficients(j)
           applyFeaturesStd match {
             case Some(getStd) =>
+              // If `standardization` is false, we still standardize the data
+              // to improve the rate of convergence; as a result, we have to
+              // perform this reverse standardization by penalizing each component
+              // differently to get effectively the same objective function when
+              // the training dataset is not standardized.
               val std = getStd(j)
               if (std != 0.0) {
                 val temp = coef / (std * std)
@@ -66,6 +75,7 @@ private[ml] class L2Regularization(
                 0.0
               }
             case None =>
+              // If `standardization` is true, compute L2 regularization normally.
               sum += coef * coef
               gradient(j) = coef * regParam
           }