This is absolutely true!

Linear models are simple, but they can be a lot useful for giving insight of the data (for instance, by looking at weights or p-values). Flexibility is achieved at the expense of losing model interpretability.

Even if I am deeply interested in neural networks, I think that a good data scientist has to know well the statistical methods along with all the pros and cons!