Note that in some cases you could use adaptive filters, that do not need to be explicitly trained. Examples of adaptive filters includes Least Mean Squares, Recursive Least Squares, Kalman...
The subtle distinction between adaptive filters and traditional ML algorithms (like the ones that can be found in scikit-learn) is that the former do not follow the fit on training data -> deploy scheme for most use cases, i.e. you won't have to create train/test splits for a given set of hyperparameters.
As an example:
if your data is a time series and you want to estimate another time series that depends on the former, then you can use an RLS filter that will iteratively adapt its weights, instead of explicitly using least squares minimization to find coefficients over some split of the data.