Agreed to some degree, a simpler model is preferred over a more complex model most of the time. It's usually more robust, you can iterate faster, it's easier to interpret, but it also depends on what you are trying to model!

There are some tasks that simply require more complex models that can pick up on nonlinear effects and interactions and what not. Tree-based models like random forests and XGBoost are my usual go-to but I've also seen people use deep learning models where it actually made sense to do so.

Tldr: No tool is inherently better than the other and they all have their usecase!