Kernel smoothing is a non-parametric technique used to estimate the probability density function of a random variable or to smooth data points in a dataset. It achieves this by averaging data points within a defined neighborhood, weighted by a kernel function, which helps in revealing the underlying structure of the data without assuming any specific data distribution.