Uh, oh, old question... !
A linear regression model usually has one or more of three purposes:
- Effect estimation
- Testing hypotheses
While there is no multicollinearity assumption behind the classic normal linear model, strong multicollinearity is problematic regarding the first two items:
- Effect estimates are unnatural due to Ceteris Paribus clause, so are corresponding hypothesis tests.
- Multicollinearity blows up standard errors of estimated effects, which influences p values and confidence intervals accordingly.
Predictions are unaffected. So if bullet point 3) is the only purpose of your model, you can usually enjoy life even under strong multicollinearity.
For boosted trees, it is quite the same:
If you are only after predictions, multicollinearity is no problem.
However, if you use your model to derive information on the effects (e.g. by studying partial dependence plots, any sort of variable importance, SHAP decompositions etc.), multicollinearity is similarly problematic as for linear regression.
You don't do hypothesis tests with boosted trees, so bullet point 2) is irrelevant here.