The Gaussian Kernel is a popular function used in machine learning and statistics, particularly in support vector machines and kernel methods. It transforms data into a higher-dimensional space, allowing for better separation of classes. The kernel is defined by a bell-shaped curve, which emphasizes points closer to the center while diminishing the influence of points further away. Mathematically, the Gaussian Kernel is expressed as K(x, y) = \exp\left(-\frac\|x - y\|^22\sigma^2\right) , where x and y are data points, and \sigma controls the width of the kernel. This property makes it effective for capturing complex relationships in data, enhancing the performance of various algorithms.