We introduce mlOSP, a computational template for machine learning for optimal stopping problems, which is implemented in the R statistical environment and publicly available via a GitHub repository. mlOSP presents a unified numerical implementation of regression Monte Carlo (RMC) approaches to optimal stopping, providing a state-of-the-art, open-source, reproducible and transparent platform. Highlighting the modular nature of the platform, we present multiple novel variants of RMC algorithms, both in terms of constructing simulation designs for training the regressors and in terms of machine learning regression modules. Further, mlOSP nests most of the existing RMC schemes, allowing for a consistent and verifiable benchmarking of extant algorithms. The paper contains extensive R code snippets and figures and serves as a vignette of the underlying software package.