Support Vector Machines (SVM) are a powerful supervised machine learning algorithm primarily used for classification and regression tasks. They work by identifying a hyperplane that best separates data points of different classes in a high-dimensional space. The key concept behind SVM is the use of support vectors, which are the data points closest to the hyperplane and significantly influence its position. By maximizing the margin between these support vectors and the hyperplane, SVM effectively creates a robust decision boundary. This method is particularly effective for complex datasets, including those with non-linear relationships, making SVM a popular choice in various applications.