Kernel methods are a class of algorithms used in machine learning that enable the analysis of data in high-dimensional spaces without explicitly transforming the data. They work by applying a mathematical function, called a kernel, to compute the similarity between data points, allowing for complex relationships to be captured. This is particularly useful in tasks like classification and regression. One of the most popular kernel methods is the Support Vector Machine (SVM), which uses kernels to find the optimal hyperplane that separates different classes in the data. Other examples include Gaussian processes and kernel principal component analysis, which leverage the kernel trick to enhance performance in various applications.