Won't trying different combinations of hyper parameters/lambda (over a small range) help us arrive better instead of manually tuning it? Or is that what the author meant by manual tuning?
As I understand it, one of the pitfalls of automatic tuning is that it becomes hard to account for seasonality and you will likely end up with useless parameters - for instance a customer ID is rarely a good parameter to optimize on, even as a categorical variable, except in very specific cases. It is probably a proxy variable for one or more other ones that you need to tease out of the rest of the data.
(warning, potentially me talking nonsense coming up) Automatic tuning is no substitute for a talented analyst who knows the data well and understands the goal. But if you've got hundreds to millions of parameters, you may not have another choice really.