A Support Vector Machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. SVM works by finding the hyperplane that best separates the data points of different classes with the maximum margin. This hyperplane is defined by support vectors, which are the data points closest to the decision boundary. SVM can handle both linear and non-linear classification tasks using kernel functions, which map the input data into higher-dimensional spaces. SVMs are known for their effectiveness in high-dimensional spaces and are widely used in applications such as image recognition, text classification, and bioinformatics.