Gibbs sampling is a statistical technique used to generate samples from a complex probability distribution. It works by breaking down the distribution into simpler, conditional distributions. By iteratively sampling from these conditional distributions, Gibbs sampling allows us to explore the overall distribution without needing to calculate it directly. This method is particularly useful in Bayesian statistics and machine learning, where we often deal with high-dimensional data. By focusing on one variable at a time while keeping others fixed, Gibbs sampling helps in approximating the joint distribution, making it easier to analyze and draw conclusions from the data.