Github user yanboliang commented on the issue:
https://github.com/apache/spark/pull/16011
@MLnick Your description is totally correct. However, the ```model``` you
used in your example is type of ```Bucketizer```. I will keep
```handleInvalid``` in ```Bucketizer```. In the current ML code,
```QuantileDiscretizer```(which is an estimator) will produce
```Bucketizer```(which is a model), and the invalid data handling only happens
when you use ```Bucketizer``` to transform some data(may be not the same data
used for training). When you run ```QuantileDiscretizer```, all NaN values will
be ignored and no any error raised. That is the cause why I propose to remove
```handleInvalid``` from ```QuantileDiscretizer```.
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