Random Forest is an ensemble learning method primarily used for classification and regression tasks, which constructs multiple decision trees during training and outputs the mode of their classes or mean prediction. It enhances predictive accuracy and controls overfitting by averaging multiple deep decision trees, trained on different parts of the same dataset with replacement.