Ensemble learning is a machine learning paradigm where multiple models, often referred to as 'weak learners', are combined to produce a stronger, more accurate model. This approach leverages the diversity among individual models to reduce overfitting and improve predictive performance by aggregating their predictions through techniques like bagging, boosting, or stacking.