In unsupervised learning, where no training takes place, one simply hopes that the unsupervised learner will work well on any unlabeled test collection.
when the variability in the data is large, such hope may be unrealistic; a tuning of the unsupervised algorithm may then be necessary in order to perform well on new test collections.