[SPARK-13545][MLLIB][PYSPARK] Make MLlib LogisticRegressionWithLBFGS's default parameters consistent in Scala and Python

## What changes were proposed in this pull request?
* The default value of ```regParam``` of PySpark MLlib ```LogisticRegressionWithLBFGS``` should be consistent with Scala which is ```0.0```. (This is also consistent with ML ```LogisticRegression```.)
* BTW, if we use a known updater(L1 or L2) for binary classification, ```LogisticRegressionWithLBFGS``` will call the ML implementation. We should update the API doc to clarifying ```numCorrections``` will have no effect if we fall into that route.
* Make a pass for all parameters of ```LogisticRegressionWithLBFGS```, others are set properly.

cc mengxr dbtsai
## How was this patch tested?
No new tests, it should pass all current tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #11424 from yanboliang/spark-13545.

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