A Decision Tree is a supervised learning algorithm used for classification and regression tasks. It works by splitting data into branches based on feature values, eventually leading to decision points (leaves) that represent predictions. Decision trees are easy to interpret and useful for rule-based modeling, but they can be prone to overfitting without pruning or using ensemble methods like Random Forests.