This is an example of using pipelining in sklearn.
import numpy as np
from sklearn.datasets import fetch_olivetti_faces
from sklearn.svm import SVC
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
dataset = fetch_olivetti_faces()
X = dataset.data
y = dataset.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.1, random_state=42)
clf = Pipeline([('scaler', StandardScaler()), ('linear_model', SVC(kernel='linear'))])
clf.fit(X_train, y_train)
Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_model', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0, kernel='linear', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False))])
y_pred = clf.predict(X_test)
print "Accuracy score is {}".format(accuracy_score(y_test, y_pred))
Accuracy score is 1.0