Machine learning is the hacker art of describing the features of instances that we want to make predictions about, then fitting the data that describes those instances to a model form. Applied machine learning has come a long way from it's beginnings in academia, and with tools like Scikit-Learn, it's easier than ever to generate operational models for a wide variety of applications. Thanks to the ease and variety of the tools in Scikit-Learn, the primary job of the data scientist is model selection. Model selection involves performing feature engineering, hyperparameter tuning, and algorithm selection. These dimensions of machine learning often lead computer scientists towards automatic model selection via optimization (maximization) of a model's evaluation metric. However, the search space is large, and grid search approaches to machine learning can easily lead to failure and frustration. Human intuition is still essential to machine learning, and visual analysis in concert with automatic methods can allow data scientists to steer model selection towards better fitted models, faster. In this talk, we will discuss interactive visual methods for better understanding, steering, and tuning machine learning models.