Kernel Density Estimation (KDE) is a statistical technique used to estimate the probability density function of a random variable. It smooths out data points by placing a "kernel" function, often a Gaussian, over each point, allowing for a continuous representation of the data distribution. This helps visualize the underlying structure of the data without assuming a specific distribution shape. KDE is particularly useful in exploratory data analysis, as it provides insights into the data's distribution, such as identifying peaks and valleys. It can be applied in various fields, including data science, finance, and ecology, to analyze patterns and trends effectively.