Stacking and blending are meta-ensemble techniques that combine the predictions of multiple base models using a learned meta-model, rather than simple averaging. While averaging (bagging) treats all base models as equals and blending fixes weights by hand, stacking lets the data decide how to combine model predictions optimally, using a second-level model (the meta-learner) trained on the base models' outputs. The result is often stronger than any individual model or naive average, because the meta-learner discovers which base models are more reliable in which regions of the feature space.
Stacking consistently appears in winning competition solutions and is increasingly used in production ensemble systems. Its appeal is that it is theoretically grounded — it learns the optimal combination function from data — and practically powerful — it can capture synergies between models with completely different inductive biases (a tree model and a linear model may each capture aspects of the true pattern that the other misses). Understanding how to implement stacking correctly, avoiding the target leakage that corrupts naive implementations, is the key skill.