On the base of the least-squares technique the universal method of regularized identification is developed. For any given input and output in the presence of the worst-case bounded noise it provides the convergent parameter estimate when the order of stable model is increased. This implies that even in the case when available information is not sufficient to identify the system it is possible to evaluate an approximate model using the approach offered in the paper. The recurrent form of regularized least-squares algorithm with adaptive regularization depending on incoming data is constructed.