scikit-learn doesn't have a strong neural network codebase -- for anything not NN based they've largely got you covered (along with good infrastructure tooling for pipelines, cross validation, hyper-parameter searching etc.). Contrary to the impression you may get if you only follow the current buzzwords there is a great deal of value in machine learning right now beyond NNs and deep learning. On the other hand if deep learning is what you want to do, scikit-learn is not currently the best library for that.
scikit-learn would likely fall into the category of "black box" frameworks the author mentions. If I understand correctly, this book will let the reader gain an understanding of the underlying algorithms from an intuitive standrpoint.
It is worth noting that scikit-learn does value clear understandable implementations, so you can actually pop open the source code and expect to find something other than a black box. Now, in many cases you'll have optimization work that means a slightly less obvious approach is taken, but the scikit-learn maintainers do work hard to try and ensure that, if you want to learn, you should be able to open up the code and do so.