The issue is that generic machine learning algorithms work ok enough as black boxes, but to squeeze top performance out of them you need to do feature engineering, architecture/model structure futzing, method selection, etc, and in practice there are far too many of these meta-hyperparameters to tune with cross-validation or something similar.

While the generic ML tools work really well, it takes domain knowledge to find the best way of applying them to the problem at hand, specially since it almost never fits into the classification/regression from IID training training data model that most algorithms are designed based on. At first this might seem counter-intuitive to you, but I've seen dramatic reductions in the error rate just from picking good features or a reasonable model structure in a way that's not easy to automate. And while deep learning or structure learning tries to address these problems, there are issues with nonconvexity and really long training times that make these algorithms unrealistic in many situations (and, consequently, make them underperform simpler methods with clever domain engineering).