A. Ensemble learning in Random Forests involves building multiple decision tree models using different subsets of the training data. This technique, known as Bagging (Bootstrap Aggregating), reduces overfitting and improves prediction accuracy by averaging the results (regression problems) or using majority voting (classification tasks). Random Forests are particularly effective with large datasets and diverse predictors.