OLS is my default go to. It outperforms a random forest so often in small data real world applications over nonstationary data, and model explainability is buil

OLS is my default go to. It outperforms a random forest so often in small data real world applications over nonstationary data, and model explainability is built into it. If I'm working in a domain with stationary data then I'd tilt more to the forest (due to not having to engineer features, and the inbuilt ability to detect non-linear relationships and interactions between features).