Decision Trees are a method of supervised learning used for classification and regression tasks. They operate by splitting data into subsets based on the value of input features. Each internal node represents a decision on an attribute, each branch represents the outcome of the decision, and each leaf node represents a class label or numerical value. This visually intuitive model can easily handle both categorical and numerical data, making it versatile. It's related to Random Forests, which are ensembles of Decision Trees to improve robustness and accuracy.