Decision Trees are a popular supervised learning algorithm used for both classification and regression tasks in machine learning. They operate by recursively splitting data into subsets based on feature values, forming a tree-like structure that aids in making predictions. Each internal node represents a test on a feature, branches indicate the outcomes, and leaves signify the final predictions or class labels. Decision Trees are favored for their intuitive nature, ease of interpretation, and minimal data preprocessing requirements. However, they can be prone to overfitting and may require techniques like pruning to enhance their performance and generalization capabilities.