This lecture will discuss meta-models (also called surrogate models or emulators) and their role in emulation of computer models. This lecture will focus on one particular emulator, the Gaussian Process (GP) Model. We will discuss the functional form of the GP, how GPs differ from other models, how to estimate the parameters governing the GP, training set design, and software tools available. We will conclude with the use of GPs in various contexts: in experimental design, optimization, and calibration.