It depends on your domain. If you have a relatively small number of features and your features have relatively stable but not necessarily linear partial depend

It depends on your domain. If you have a relatively small number of features and your features have relatively stable but not necessarily linear partial dependence, then shap can unpack them pretty well. E.g. value of a house it might not necessarily increase linearly in squarefoot, distance from city centre and other factors so a random-forest/gbt might do better than a linear model without careful feature engineering. Shap is a brilliant tool to unpack the dependencies this model has learned. There will be other cases where a linear model is adequate, so you can get the explainability just from the coefficients, or where there are very large numbers of features with complicated nested structure. In either case shap is not too useful. But the 'fit lightgbm and explain it with shap' is a decently pragmatic approach for lots of tabular data machine-learning problems.