This talk will start with a very brief introduction to R and the main concepts of this data analysis environment and programming language. We will then shift focus to predictive tasks and models obtained from data to solve these tasks. Finally, the main topic of the talk will be on how to solve the critical issue of estimating the predictive performance of alternative models to solve some task. This estimation process is key to answer the question of which model is the "best" for a problem we are facing. We will describe the facilities provided by the R package performanceEstimation to address this model selection problem and provide some illustrative case studies. We wrap up with the ongoing plans of interfacing this package to OpenML.