The Kernel Method is a technique used in machine learning and statistics to analyze data in a high-dimensional space without explicitly transforming the data. It relies on a mathematical function called a kernel, which computes the similarity between data points. This allows algorithms, like Support Vector Machines and Principal Component Analysis, to find patterns and make predictions more effectively. By using the Kernel Method, we can handle complex data structures, such as non-linear relationships, while maintaining computational efficiency. Common kernels include the Gaussian kernel and the polynomial kernel, each suited for different types of data and tasks, enhancing the model's performance.