Does using sparse matrices make an algorithm slower or faster in Sklearn?
If your data are sparse, the extra tree classifier will be faster with a csc_matrix. In doubt, I would suggest you to benchmark with both version. 
All algorithms should benefit from using the appropriate sparse format if your data are sufficiently sparse. For instance, algorithms based on dot product will be a lot faster with sparse data.
I have a large but sparse train data. I would like to use it with ExtraTreeClassifier. I am not sure considering computational time whether I need to use sparse csr_matrix or the raw data. Which version of the data runs faster with that classifier and can we generalize its answer to all sparse capable models?