A kernel function is a mathematical tool used in machine learning, particularly in algorithms like Support Vector Machines and Gaussian Processes. It transforms data into a higher-dimensional space, allowing for better separation of data points that are not linearly separable in their original space. This transformation helps in identifying patterns and relationships within the data. Kernel functions can take various forms, such as linear, polynomial, or radial basis function (RBF). Each type has its own characteristics and is chosen based on the specific problem at hand. By using kernel functions, models can achieve improved accuracy and performance in classification and regression tasks.