The goal of this project is to provide users of the mlr package with a way to visualize what happens during the tuning process that identifies the best hyperparameters for a learner given the data. Looking at popular packages in the machine learning community such as sklearn or caret, the model evaluation visualization process either requires lots of boilerplate code or is sparsely implemented across packages. This project will rectify the situation for R users of the mlr package by implementing three new features: plotting of a single hyperparameter against a scoring function, plotting multiple hyperparameters and scoring functions, as well as support for ablation analysis. The design of the features will focus on ease of use for the end user and customization. Users can choose to leverage automatically generated plots or use the data in some other manner downstream.