The Epanechnikov Kernel is a type of kernel function used in statistics for non-parametric density estimation. It is defined by a parabolic shape and is particularly efficient for estimating the probability density function of a random variable. The kernel is zero outside a certain range, which helps to reduce the influence of distant data points. This kernel is favored for its optimal properties in minimizing mean integrated squared error compared to other kernels. Its shape allows for a balance between bias and variance, making it a popular choice in various applications, including machine learning and data analysis.