Support Vector Machines (SVM) are a powerful supervised learning algorithm primarily used for classification and regression tasks in machine learning. They work by identifying the optimal hyperplane that separates different classes in a dataset, maximizing the margin between the closest data points, known as support vectors. This approach allows SVMs to effectively handle both linear and non-linear decision boundaries, making them versatile for various applications. Additionally, SVMs are well-suited for high-dimensional data and can be utilized for outlier detection, enhancing their utility in complex datasets.