RooFit is a C++ library designed to facilitate data modeling in the ROOT environment. It represents mathematical concepts such as variables, probability density functions (PDFs), and integrals as C++ objects, providing a flexible framework for building complex fit models. The library supports various functionalities, including fitting (binned and unbinned likelihood, $\chi^2$), plotting, and toy Monte Carlo generation. RooFit has matured into an industrial-strength tool capable of handling complex fits, as evidenced by its use in the BABAR experiment. It is now available to all users on SourceForge. The introduction highlights the central challenge in physics analysis: accurately modeling observable quantities in terms of physical parameters and detector effects. The development of suitable models and the tools to exploit them is often a bottleneck in physics analyses. RooFit addresses this by providing a general-purpose toolkit for physics analysis modeling. The overview section explains that RooFit extends the ROOT analysis environment by providing a language to describe data models. It emphasizes the natural and self-documenting vocabulary for building models, the object-oriented nature of the implementation, and the automatic normalization of PDFs. The library supports the composition of complex models from elementary PDFs and offers tools for parameterized Monte Carlo event generation. The article then discusses specific use cases, including one-dimensional yield fits, simple Monte Carlo studies, and multi-dimensional models. It covers fitting, plotting, and event generation for both one-dimensional and multi-dimensional data. Advanced fitting options, such as custom goodness-of-fit expressions, are also covered. RooFit's efficiency and optimal function calculation capabilities are highlighted, including optimization techniques like value caching, caching and lazy evaluation, factorization, and parallelization. The library also provides data and project management tools to ease the creation and management of large numbers of datasets and PDFs. The development trajectory and status section traces the evolution of RooFit from its initial release in 1999 to its current form, emphasizing the redesign efforts and the addition of core features. Since its migration to SourceForge in 2002, RooFit has gained widespread adoption within the HEP community. Finally, the current use and prospects section discusses the adoption of RooFit within the BaBar experiment and its steady growth in other HEP collaborations.RooFit is a C++ library designed to facilitate data modeling in the ROOT environment. It represents mathematical concepts such as variables, probability density functions (PDFs), and integrals as C++ objects, providing a flexible framework for building complex fit models. The library supports various functionalities, including fitting (binned and unbinned likelihood, $\chi^2$), plotting, and toy Monte Carlo generation. RooFit has matured into an industrial-strength tool capable of handling complex fits, as evidenced by its use in the BABAR experiment. It is now available to all users on SourceForge. The introduction highlights the central challenge in physics analysis: accurately modeling observable quantities in terms of physical parameters and detector effects. The development of suitable models and the tools to exploit them is often a bottleneck in physics analyses. RooFit addresses this by providing a general-purpose toolkit for physics analysis modeling. The overview section explains that RooFit extends the ROOT analysis environment by providing a language to describe data models. It emphasizes the natural and self-documenting vocabulary for building models, the object-oriented nature of the implementation, and the automatic normalization of PDFs. The library supports the composition of complex models from elementary PDFs and offers tools for parameterized Monte Carlo event generation. The article then discusses specific use cases, including one-dimensional yield fits, simple Monte Carlo studies, and multi-dimensional models. It covers fitting, plotting, and event generation for both one-dimensional and multi-dimensional data. Advanced fitting options, such as custom goodness-of-fit expressions, are also covered. RooFit's efficiency and optimal function calculation capabilities are highlighted, including optimization techniques like value caching, caching and lazy evaluation, factorization, and parallelization. The library also provides data and project management tools to ease the creation and management of large numbers of datasets and PDFs. The development trajectory and status section traces the evolution of RooFit from its initial release in 1999 to its current form, emphasizing the redesign efforts and the addition of core features. Since its migration to SourceForge in 2002, RooFit has gained widespread adoption within the HEP community. Finally, the current use and prospects section discusses the adoption of RooFit within the BaBar experiment and its steady growth in other HEP collaborations.