cross_val_score meaning I'm studying the following code, which cross_val_score_ was used as well as .mean() and .std(). I read many documentation of the meanings, but didn't get what each of the above does.
import pandas as pd 
import numpy as np
from sklearn import tree 
import graphviz
from sklearn.model_selection import cross_val_score

#importing the dataset
d = pd.read_csv('student-por.csv', sep= ';')

d['pass'] = d.apply(lambda row: 1 if (row['G1']+ row['G2']+ row ['G3']) &gt;= 35 else 0 , axis=1)
d = d.drop(['G1', 'G2','G3'], axis=1 )

#Doing one-hot encoding
d=pd.get_dummies(d, columns =['sex','activities','school', 'address', 'famsize','Pstatus','Mjob','Fjob','reason','guardian','schoolsup','famsup','paid','nursery','higher','internet','romantic'])

#shuffle rows
d = d.sample(frac=1)

#split traning and test
d_train = d[:500]
d_test = d[500:]

d_train_att = d_train.drop(['pass'], axis=1)
d_train_pass= d_train['pass']

d_test_att = d_test.drop(['pass'], axis=1)
d_test_pass= d_test['pass']

d_att = d.drop(['pass'], axis=1)
d_pass = d['pass']

t = tree.DecisionTreeClassifier(criterion ='entropy', max_depth = 5)
t= t.fit (d_train_att, d_train_pass)

#to export the tree
dot_data = tree.export_graphviz(t,out_file = 'students-tree.png', label ='all', impurity=False, proportion= True, feature_names=list(d_train_att), class_names=['fail', 'pass'], filled = True, rounded=True)

t.score (d_test_att, d_test_pass)
scores = cross_val_score(t, d_att,d_pass, cv=5)

print ('Acuracy %0.2f (+/- %0.2f)' % (scores.mean(), scores.std() *2))

in short this is what I need to know:
scores = cross_val_score(t, d_att,d_pass, cv=5)

print ('Acuracy %0.2f (+/- %0.2f)' % (scores.mean(), scores.std() *2))

one more thing, am I suppose to get the same score as in the original code publisher? because I didn't.
The source, around line 274 is where the default scoring for cross_validation_score gets set, if you pass in None for the scorer argument.  For classifiers, the usual default score is accuracy.  For regression, it's rmse, IIRC.  So, since you're applying a decision tree classifier, cross_val_score splits the data into 5 equalish sized pieces, trains on each combination of 4 and gives back the accuracy of the estimator on the 5th.  The mean and std of these accuracies presumably tells one something about the performance of the family of decision tree classifiers on your dataset, but I would take it with a grain of salt.