Support Vector Machines (SVM) are supervised learning models used for classification and regression tasks, known for their ability to find the optimal hyperplane that maximizes the margin between different classes in a dataset. They are particularly effective in high-dimensional spaces and are robust to overfitting, especially in cases where the number of dimensions exceeds the number of samples.