Kernel density estimation (KDE) is a non-parametric method to estimate the probability density function of a random variable, allowing for a smoothed representation of data distribution. It is particularly useful for visualizing the underlying structure of data without assuming a specific parametric form, making it a flexible tool in exploratory data analysis.