Bagging involves training multiple models independently on random subsets of data and then combining their predictions through a majority vote. Boosting focuses on correcting the errors made by previous weak models in the sequence to create a stronger model. Stacking combines multiple models by training a meta-model, which takes model predictions as input and outputs the final prediction.