A Gaussian process is a statistical method used to model and predict data that can vary over time or space. It treats a collection of random variables, any finite number of which have a joint Gaussian distribution. This means that the values of the process at different points are correlated in a way that can be described by a mean function and a covariance function. Gaussian processes are particularly useful in machine learning for tasks like regression and classification. They provide a flexible way to capture uncertainty in predictions, allowing for the incorporation of prior knowledge and the ability to make predictions with confidence intervals.