The use of Multidimensional Unfolding is most appropriate when the goal of your analysis is to find the structure in a set of distance measures between two sets of objects (referred to as the row and column objects). This is accomplished by assigning observations to specific locations in a conceptual low-dimensional space so that the distances between points in the space match the given (dis)similarities as closely as possible. The result is a least-squares representation of the row and column objects in that low-dimensional space, which, in many cases, will help you further understand your data.
Relation to other Categories procedures. If your data consist of distances between a single set of objects (a square, symmetrical matrix), use Multidimensional Scaling.
Relation to standard techniques. The Categories Multidimensional Unfolding procedure (PREFSCAL) offers several improvements upon the unfolding functionality available in the Statistics Base option (through ALSCAL). PREFSCAL allows you to put restrictions on the common space; moreover, PREFSCAL attempts to minimize a penalized stress measure that helps it to avoid degenerate solutions (to which older algorithms are prone).