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 data point. The sum of these kernels creates a continuous curve that represents the distribution of the data. KDE is particularly useful for visualizing the distribution of data in a non-parametric way, meaning it does not assume a specific underlying distribution. This method helps identify patterns, such as peaks and valleys, in the data, making it easier to understand the underlying structure of the dataset.