Random Forests 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 model accuracy and controls overfitting by averaging the results of deep, unpruned trees that are trained on different subsets of data and features.