Vector Machines, specifically Support Vector Machines (SVMs), are a powerful class of supervised learning algorithms used primarily for classification and regression tasks in machine learning. They work by finding the optimal hyperplane that separates different classes in a high-dimensional space, maximizing the margin between the closest data points of each class, known as support vectors. SVMs are particularly effective in high-dimensional spaces and can handle cases where the number of dimensions exceeds the number of samples. Their versatility is enhanced by the ability to use various kernel functions, allowing them to model complex relationships in data.