The current, rather excessive, employment of deep learning methods is majorly motivated by the desire to understand them better through the experience gained in applying them.

A good paper that puts this into perspective is from Lea Breiman called "Statistical Modeling: The Two Cultures". He argues in the paper that data based statistical models are preventing statisticians from new and exciting discoveries with algorithmic models. Coincidentally, the author is the creator of the ensemble idea that you are using here as explanation. Now take into account that this was written in 2001 where ensembles were what deep learning is in 2022.

Basically, deep learning is preferred in order to improve it to a point where it will by far outperform all other methods, which it is believed to have the potential for. For it may one day lead us to new and exciting discoveries.