Benefits of using Deep Learning-specific hyperparameter optimization tools vs. sklearn? There are quite a few library for hyperparameter optimization that are specific to Keras or other Deep Learning libraries, like Hyperas or Talos.

My question is, what's the main benefit of using these libraries compared to, for example, sklearn.model_selection.GridSearchCV() or sklearn.model_selection.RandomizedSearchCV?
In my case with hyperas I noticed one of the distinct advantage over gridsearch, that is, gridsearch function takes only one array as input. My requirement was two be able to send two array as input as I am working with siamese network. I could do it with hyperas out of the box. So hyperas is more flexible than gridsearchcv.  Check this example