Importance sampling is a statistical technique used to estimate properties of a particular distribution while only having samples from a different distribution, by re-weighting the samples according to their likelihood under the target distribution. This method is particularly useful in situations where direct sampling from the target distribution is difficult or computationally expensive, such as in Bayesian inference and Monte Carlo simulations.