Decision Trees are a popular method used in machine learning for making decisions based on data. They work by splitting the data into branches based on feature values, leading to a final decision or classification at the leaves of the tree. This structure makes it easy to visualize and understand the decision-making process. One of the key advantages of Decision Trees is their interpretability; users can easily follow the path from the root to the leaves to see how decisions are made. They can handle both numerical and categorical data, making them versatile for various applications in fields like finance, healthcare, and marketing.