Auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction.
Auto-sklean is based on the definition of CASH (Combined Algorithm Selection and Hyperparameter optimization) problem used by Auto-Weka and the same idea behind Azure Automated ML: they consider the problem of simultaneously selecting a learning algorithm and setting its hyper-parameters. The main difference they propose is to incorporate two extra steps to the main process: a meta-learning step at the beginning and an automated ensemble construction step at the very end.