A Gaussian Process (GP) is a statistical method used for making predictions about uncertain data. It treats a collection of random variables, any finite number of which have a joint Gaussian distribution. This means that it can model complex functions by defining a mean function and a covariance function, allowing it to capture the relationships between data points. GPs are particularly useful in machine learning and regression analysis because they provide not only predictions but also uncertainty estimates. This makes them valuable in fields like geostatistics and robotics, where understanding the confidence of predictions is crucial for decision-making.