Random Forest is a popular ensemble machine learning algorithm that combines multiple decision trees to create a more accurate and stable prediction model. It is primarily used for classification and regression tasks. The key concept behind Random Forest is to reduce overfitting, improve generalization, and increase model robustness by averaging the results of many decision trees.