How to scale or standardize data that is mostly 0 (ranges from 0-1)?
You don't specify, but assuming you are doing K-means clustering, the way the algorithm calculates similarity, or more technically distance, using Euclidean Distance, the distances on the dimension with the greatest magnitude will overwhelm distances of those where the values are orders of magnitude smaller.

To overcome this I would normalize the data.  I am not sure the method you use is such a big deal.  Use min-max if you are comfortable with it.  Be careful though because it is possible, if I remember correctly, in sk-learn to scale the whole data set in one go which doesn't fix the magnitude differences.  Scale each column individually or even possibly just the one that is vastly different if all the others are nominally between 0 and 1.