"Super" Optimizer concept
You could have a look at the AutoML work, which offers a few different ways to optimise a model of parameter space and goes well beyond Sklearn. They even have a tool that wraps around Sklearn!

Here is a summary from their homepage:



Here is a longer description, and here is a full example based on Keras.

These libraries can essentially combine the best known practical methods of optimisation, such as:


standard grid-search over parameters
Bayesian optimisation of parameters
a combination of the two methods: HpBandSter (HyperBand on STERoids)