What methods do you usually use to improve model performance other than feature selection, hyperparameter tuning and trying out other ML algorithms?

Say you train a model and find that the test scores are not going high. What methods do you most frequently follow from there apart from the ones mentioned in the title?

algorithms datascience feature feature selection hyperparameter ml algorithms performance test