DNN require an architecture search, I.e. the building block are full layers, depth of the network, optimizer etc.
scikit learn search a parameter space, I.e. the algorithm weight are much much simpler and few.
So to sum up, DNN search involve big building blocks while scikit learn search (or for that reason any "classical ML" algorithm) is more of a parameter search.
[ The actual sci kit learn search would also include pre processing steps, which can be seen as a separate block]
Also, note that that DNN search is much more expensive than scikit learn search (100X) ]