Random forests are an ensemble learning method that constructs multiple decision trees during training and outputs the class that is the mode of the classes of the individual trees. The document discusses random forest concepts and implementations in scikit-learn, including extreme random forests, balancing random forests to address class imbalance, using grid search to tune hyperparameters, measuring feature importance, and balancing training data through resampling techniques.