Support specifying features via multiple columns in Predictor and PredictionModel

There are always more features than one in a classification/regression task, however the current API to specify features columns in Predictor of Spark MLLib only supports one single column, which requires users to assemble the multiple features columns into a "org.apache.spark.ml.linalg.Vector" before fitting to Spark ML pipeline. 

This improvement is going to let users specify the features columns directly without vectorization. To support this, we can introduce two new APIs in both "Predictor" and "PredictionModel", and a new parameter named "featuresCols" storing the features columns names as an Array. ( PR is ready here https://github.com/apache/spark/pull/25983)
APIs:
def setFeaturesCol(value: Array[String]): M = ...
protected def isSupportMultiColumnsForFeatures: Boolean = false
Parameter:
final val featuresCols: StringArrayParam = new StringArrayParam(this, "featuresCols",   ...)

Then ML implementations can get and use the features columns names from this new parameter "featuresCols", along with the raw data of features in separate columns directly in dataset.