This document summarizes techniques for model selection and evaluation using visual diagnostics. It discusses using visualizations to analyze features, select algorithms, tune hyperparameters, and evaluate model performance. Key aspects covered include using visualizations to identify important and correlated features, determine the best number of clusters or regularization parameter value, and evaluate classifier performance metrics and regression error. The goal is to visually diagnose issues and identify the best modeling choices.