The Mat\érn model has been a cornerstone of spatial statistics for more than half a century. More recently, the Mat\érn model has been central to disciplines as diverse as numerical analysis, approximation theory, computational statistics, machine learning, and probability theory. In this article we take a Mat\érn-based journey across these disciplines. First, we reflect on the importance of the Mat\érn model for estimation and prediction in spatial statistics, establishing also connections to other disciplines in which the Mat\érn model has been influential. Then, we position the Mat\érn model within the literature on big data and scalable computation: the SPDE approach, the Vecchia likelihood approximation, and recent applications in Bayesian computation are all discussed. Finally, we review recent devlopments, including flexible alternatives to the Mat\érn model, whose performance we compare in terms of estimation, prediction, screening effect, computation, and Sobolev regularity properties.
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