Data-driven paradigms are well-known and salient demands of future wireless communication.Em-powered by big data and machine learning techniques,next-generation data-driven communication systems will be intelligent with unique characteristics of expres-siveness,scalability,interpretability,and uncertainty awareness,which can confidently involve diversified latent demands and personalized services in the foresee-able future.In this paper,we review a promising family of nonparametric Bayesian machine learning models,i.e.,Gaussian processes (GPs),and their applications in wireless communication.Since GP models demon-strate outstanding expressive and interpretable learning ability with uncertainty,they are particularly suitable for wireless communication.Moreover,they provide a natural framework for collaborating data and em-pirical models (DEM).Specifically,we first envision three-level motivations of data-driven wireless com-munication using GP models.Then,we present the background of the GPs in terms of covariance structure and model inference.The expressiveness of the GP model using various interpretable kernels,including stationary,non-stationary,deep and multi-task kernels,is showcased.Furthermore,we review the distributed GP models with promising scalability,which is suit-able for applications in wireless networks with a large number of distributed edge devices.Finally,we list representative solutions and promising techniques that adopt GP models in various wireless communication applications.