Support Vector Machines (SVM) is a supervised machine learning algorithm that is commonly used for classification and regression tasks. SVM works by finding the hyperplane that best separates the different classes in the training data. The algorithm aims to maximize the margin between the classes, which helps improve the generalization capability of the model. SVM is known for its ability to handle high-dimensional data and deal with complex data distributions. It can also handle non-linearly separable data by using techniques like kernel functions to map the data into a higher-dimensional space. Overall, SVM is a powerful and versatile algorithm that is widely used in various fields such as image classification, text categorization, and bioinformatics. It has proven to be effective in many real-world applications and is popular among data scientists and machine learning practitioners.